Automated detection and avoidance system

ABSTRACT

In general, certain embodiments of the present disclosure provide a detection and avoidance system for a vehicle. According to various embodiments, the detection and avoidance system comprises an imaging unit configured to obtain a first image of a field of view at a first camera channel. The first camera channel filters radiation at a wavelength, where one or more objects in the field of view do not emit radiation at the wavelength. The detection and avoidance system further comprises a processing unit configured to receive the first image from the imaging unit and to detect one or more objects therein, as well as a notifying unit configured to communicate collision hazard information determined based upon the detected one or more objects to a pilot control system of the vehicle. Accordingly, the pilot control maneuvers the vehicle to avoid the detected objects.

TECHNICAL FIELD

The present disclosure relates generally to collision detection andavoidance systems, and more specifically, to systems and methods ofautomatic collision detection and avoidance by use of a threshold image.

BACKGROUND

Unmanned aerial vehicles (UAVs), remotely piloted or self-pilotedaircrafts, have oftentimes been tasked to perform a variety of functionsbeyond the traditional surveillance and target tracking. The UAVs,although small and light-weight, can carry cameras, sensors,communications equipment, or other payloads. However, in order operatesafely in shared airspace, a LAY needs to pilot itself at a safedistance from all kinds of airborne collision hazards, e.g., mannedaircraft, other UAVs, birds, and low altitude obstacles.

Conventional automated detection and avoid systems such as TrafficCollision Avoidance System (TCAS) and Automatic DependentSurveillance-Broadcast (ADS-B) can be impractical for vehicles or UAVsof relatively smaller sizes. In particular, the use of theseconventional equipment on-board LAY may incur significant weight andpower consumption on the very limited equipment carrying capability ofUAVs. Further, the cost of equipment such as TCAS and transponders ishigh. Also, standard TCAS equipment is unable to interact withnon-cooperating flying or still (non-moving) objects that are notequipped with the counterpart equipment. Hence, standard TCAS equipmentis not able to guide UAVs out of collision under such circumstances.

Thus, there is a need of an on-board collision detection and avoidancesystem that is compact, light-weight and yet economical for UAVs toautomatically detect and avoid air traffic collisions.

SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of certain embodiments of the presentdisclosure. This summary is not an extensive overview of the disclosureand it does not identify key/critical elements of the present disclosureor delineate the scope of the present disclosure. Its sole purpose is topresent some concepts disclosed herein in a simplified form as a preludeto the more detailed description that is presented later.

In general, certain embodiments of the present disclosure providesystems, methods and vehicles for collision detection and avoidance.According to various embodiments, a detection and avoidance system for avehicle is provided comprising an imaging unit configured to obtain afirst image of a field of view at a first camera channel. The firstcamera channel filters radiation at a wavelength, where one or moreobjects in the field of view do not emit radiation at the wavelength.The detection and avoidance system further comprises a processing unitconfigured to receive the first image from the imaging unit and todetect one or more objects therein, as well as a notifying unitconfigured to communicate collision hazard information determined basedupon the detected one or more objects to a pilot control system of thevehicle.

In some embodiments, the wavelength at which the first camera channel ofthe detection and avoidance system filters radiation is within theultraviolet (UV) range, and the first camera channel filters radiationby use of a filter having a bandpass wavelength range in the ultravioletrange.

In some embodiments, the processing of the first image of the detectionand avoidance system comprises horizon detection. In some embodiments,the horizon detection comprises growing a horizon region by addingneighboring pixels to include ground objects extending from an edge ofthe horizon region.

In some embodiments, the one or more objects are detected by use ofconnected component labeling (CCL). In some embodiments, the processingof the first image further comprises selecting by a criterion, from thedetected one or more objects to exclude objects not likely collisionhazards.

In some embodiments, the detection and avoidance system furthercomprises an analyzing unit configured for determining collision hazardinformation based on the detected one or more objects. In someembodiments, the analyzing unit comprises a learning mechanism toclassify the one or more objects upon recognition.

In some embodiments, the imaging unit is further configured to obtain asecond image of a substantially same field of view at a second camerachannel, the second camera channel not filtering radiation at thewavelength. The processing unit is also further configured to identify,in the first image, one or more first regions corresponding to the oneor more objects, and to identify, in the second image, one or moresecond regions corresponding to the one or more first regions. Thedetection and avoidance system further comprises an analyzing unitconfigured to determine collision hazard information based on the one ormore first and second regions. In some embodiments, the second image isa color image.

In some embodiments, the analyzing unit comprises a learning mechanismto classify objects upon recognition. In some embodiments, the analyzingunit produces region segmentations for the one or more objects uponrecognition in addition to classifying. In some embodiments, thedetection and avoidance system is for an unmanned vehicle. In someembodiments, the detection and avoidance system is for an unmannedaerial vehicle.

In yet another embodiment of the present disclosure, a method ofdetection and avoidance by a vehicle is provided comprising obtaining afirst image of a field of view at a first camera channel. The firstcamera channel filters radiation at a wavelength, where one or moreobjects in the field of view do not emit radiation at the wavelength.The method further comprises processing the first image to detect theone or more objects, and communicating collision hazard informationdetermined based upon the detected one or more objects to a pilotcontrol system of the vehicle.

In some embodiments, the wavelength at which the first camera channelfilters radiation is within the ultraviolet range, and the first camerachannel filters radiation by use of a filter having a bandpasswavelength range in the ultraviolet range.

In some embodiments, the processing of the first image comprises horizondetection. In some embodiments, the horizon detection comprises growinga horizon region by adding neighboring pixels to include ground objectsextending from an edge of the horizon region. In some embodiments, theone or more objects are detected by use of connected component labeling(CCL). In some embodiments, the processing of the first image furthercomprises selecting, by a criterion, from the detected one or moreobjects to exclude objects that are not likely collision hazards.

In some embodiments, the method further comprises communicating thedetected one or more objects to an analyzing unit to determine collisionhazard information. In some embodiments, the analyzing unit comprises alearning mechanism to classify the one or more objects upon recognition.

In some embodiments, the method further comprises obtaining a secondimage of a substantially same field of view at a second camera channel,the second camera channel not filtering radiation at the wavelength. Themethod also comprises identifying, in the first image, one or more firstregions corresponding to the one or more objects, and identifying, inthe second image, one or more second regions corresponding to the one ormore first regions. The method further comprises communicating the oneor more first and second regions to an analyzing unit to determinecollision hazard information. In some embodiments, the second image is acolor image.

In some embodiments, the analyzing unit comprises a learning mechanismto classify objects upon recognition. In some embodiments, the analyzingunit produces region segmentations for the one or more objects uponrecognition in addition to classifying.

In some embodiments, the method further comprises performing maneuver toavoid the detected one or more objects. In some embodiments, the methodmaneuvers a vehicle. In some embodiments, the vehicle is an unmannedland vehicle; in some other embodiments, the vehicle is an unmannedaerial vehicle.

In still yet another embodiment of the present disclosure, an aviationvehicle is provided comprising a pilot control system and a detectionand avoidance (DAA) system. The detection and avoidance system comprisesan imaging unit configured to obtain a first image of a field of view ata first camera channel. The first camera channel filters radiation at awavelength, where one or more objects in the field of view do not emitradiation at the wavelength. The detection and avoidance system furthercomprises a processing unit configured to receive the first image fromthe imaging unit and to detect one or more objects therein, as well as anotifying unit configured to communicate collision hazard informationdetermined based upon the detected one or more objects to a pilotcontrol system.

In some embodiments, wavelength at which the first camera channelfilters radiation is within the ultraviolet range, and the first camerachannel filters radiation by use of a filter having a bandpasswavelength range in the ultraviolet range.

In somc embodiments, the processing of the first image comprises horizondetection. In some embodiments, the processing of the first imagefurther comprises selecting, by a criterion, from the detected one ormore objects to exclude objects not likely collision hazards.

In some embodiments, the detection and avoidance system furthercomprises an analyzing unit configured for determining collision hazardinformation based on the detected one or more objects.

In sonic embodiments, the imaging unit of the detection and avoidancesystem of the aviation vehicle is further configured to obtain a secondimage of a substantially same field of view at a second camera channel,the second camera channel not filtering radiation at the wavelength. Theprocessing unit of the detection and avoidance system of the aviationvehicle is also further configured to identify, in the first image, oneor more first regions corresponding to the one or more objects, and toidentify, in the second image, one or more second regions correspondingto the one or more first regions. The detection and avoidance system ofthe aviation vehicle further comprises an analyzing unit configured todetermine collision hazard information based on the one or more firstand second regions. In some embodiments, the second image is a colorimage.

In some embodiments, the analyzing unit of the detection and avoidancesystem of the aviation vehicle comprises a learning mechanism toclassify objects upon recognition. In some embodiments, the analyzingunit of the detection and avoidance system of the aviation vehicleproduces region segmentations for the one or more objects uponrecognition in addition to classifying.

In some embodiments, the pilot control system of the aviation vehiclemaneuvers the vehicle to avoid the detected one or more objects. In someembodiments, the aviation vehicle is unmanned.

In still yet another embodiment of the present disclosure, anon-transitory computer readable medium is provided comprising one ormore programs configured for execution by a computer system fordetection and avoidance for a vehicle. The one or more programs compriseinstructions for obtaining a first image of a field of view at a firstcamera channel. The first camera channel filters radiation at awavelength, where one or more objects in the field of view do not emitradiation at the wavelength. The instructions further compriseprocessing the first image to detect the one or more objects, andcommunicating collision hazard information determined based upon thedetected one or more objects to a pilot control system of a vehicle.

In some embodiments, the wavelength at which the first camera channelfilters radiation is within the ultraviolet range, and the first camerachannel filters radiation by use of a filter having a bandpasswavelength range in the ultraviolet range.

In some embodiments, the instructions further comprise communicating thedetected one or more objects to an analyzing unit to determine collisionhazard information. In some embodiments, the analyzing unit comprises alearning mechanism to classify the one or more objects upon recognition.

In some embodiments, the instructions further comprise obtaining asecond image of a substantially same field of view at a second camerachannel, the second camera channel not filtering radiation at thewavelength. The instructions also comprise identifying, in the firstimage, one or more first regions corresponding to the one or moreobjects, and identifying, in the second image, one or more secondregions corresponding to the one or more first regions. The instructionsfurther comprise communicating the one or more first and second regionsto an analyzing unit to determine collision hazard information. In someembodiments, the second image is a color image.

In some embodiments, the analyzing unit comprises a learning mechanismto classify objects upon recognition. In some embodiments, the analyzingunit produces region segmentations for the one or more objects uponrecognition in addition to classifying. In some embodiments, the pilotcontrol system maneuvers the vehicle to avoid the detected one or moreobjects.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular embodiments of the present disclosure.

FIG. 1 illustrates a schematic block diagram of an example detection andavoidance system for a vehicle, in accordance with one or moreembodiments of the present disclosure.

FIG. 2 illustrates a sequence of intermediate images at various stagesof processing by an example detection and avoidance system, inaccordance with one or more embodiments of the present disclosure.

FIG. 3 illustrates a detailed schematic block diagram of an exampledetection and avoidance system analyzing the detected objects, inaccordance with one or more embodiments of the present disclosure.

FIGS. 4A-4B illustrates a flow chart of an example method for detectionand avoidance of collision hazards for a vehicle in accordance with oneor more embodiments of the present disclosure. In various embodiments,

FIG. 5 illustrates a perspective view of an unmanned aerial vehicle(UAV) equipped with an example detection and avoidance system and in thevicinity of another aircraft, in accordance with one or more embodimentsof the present disclosure.

FIG. 6 illustrates a schematic block diagram of an example systemcapable of implementing various processes and systems in accordance withone or more embodiments of the present disclosure.

DETAILED DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples of thepresent disclosure including the best modes contemplated by the inventorfor carrying out the present disclosure. Examples of these specificembodiments are illustrated in the accompanying drawings. While thepresent disclosure is described in conjunction with these specificembodiments, it will be understood that it is not intended to limit thepresent disclosure to the described embodiments. On the contrary, it isintended to cover alternatives, modifications, and equivalents as may beincluded within the spirit and scope of the present disclosure asdefined by the appended claims.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.Particular example embodiments of the present disclosure may beimplemented without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present disclosureunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present disclosure will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

The present disclosure provides a detection and avoidance system for avehicle such as an unmanned aerial vehicle (UAV) to detect collisionhazardous objects using a threshold (first) image to mask out thresholdobjects posing collision hazards. Upon detection of the thresholdobjects, the detection and avoidance system notifies a pilot controlsystem such as an autopilot control system of the vehicle to performavoidance maneuvers in accordance with the collision hazard informationdetermined based on the detected objects.

In some embodiments, the detection and avoidance system furthercomprises an analyzing unit which employs machine learning capabilitiesto recognize the threshold objects detected thereby. As such,classification information with respect to the detected objects isfurther identified and therefore utilized in determining the collisionhazard information communicated to the pilot control system of thevehicle. In some embodiments, the detection and avoidance system furtherincludes a machine learning system trained for detecting objects in thefirst image.

EXAMPLE EMBODIMENTS

FIG. 1 illustrates a schematic block diagram of an example detection andavoidance system for a vehicle in accordance with one or moreembodiments of the present disclosure. The detection and avoidancesystem 100 communicates collision hazard information to a pilot controlsystem 150, such as an auto pilot control system, of the vehicle so thatthe vehicle is maneuvered accordingly to avoid collisions with thedetected objects posing collision hazards. As shown herein, the system100 includes a first camera channel 112, at which only radiation orlight at a certain wavelength is allowed to pass through for an imagingunit 102 to capture a first image of a field of view. In other words,the first image records the field of view by use of light or radiationat the certain designated wavelength only, with radiation or light atwavelengths other than the certain wavelength being filtered out. Insome embodiments, the first image is generated as a binary image, wherea given pixel is either “ON” or “OFF”. For example, pixels may belabeled “ON” if they are black or dark; and “OFF” if they are white orbright. In some embodiments, the first image is thresholding into abinary image, where pixels of values greater than a pre-determinedthreshold value are labeled “ON;” and “OFF” if less than thepre-determined threshold value.

As some objects in the field of view do not emit or re-emit radiation atthat wavelength, the first image captured at the first camera channel112 represents those objects as dark pixels or ON pixels. On thecontrary, areas or regions illuminated by radiation or light at thecertain designated wavelength, as well as objects emitting orre-emitting radiation at the certain designated wavelength, arerepresented in the first image as white pixels or OFF pixels. Forexample, the sun being an UV radiation source, sunlight-illuminated skyis captured in a UV photograph as a white background or in OFF pixels.At the same time, an airborne aircraft in the afore-mentioned sky isotherwise captured in dark or ON pixels as the aircraft blocks theradiation in the UV range from the sun, and the aircraft does not emitor re-emit UV radiation, Various UV filters which allow light in the UVrange to pass while absorbing or blocking visible and infrared light canbe used for UV photography at the first camera channel. Such UV filterscan be made from special colored glass and/or may be coated withadditional filter glass to further block unwanted wavelengths.

In some embodiments, the certain wavelength designated at the firstcamera channel 112 is within the ultraviolet (UV) range. In someembodiments, at the first camera channel 112, radiation within the UVrange is captured by use of an UV filter 112A having a bandpasswavelength range in the ultraviolet (UV) range. In some embodiments,such an exemplary UV filter may be a Baader-U filter model #2458291available from Baader Planetarium GmbH in Mammendorf, Germany, or aStraightEdgeU ultraviolet bandpass filter. model 379BP52 available fromUVR Defense Tech, Ltd. in Wilton, New Hampshire, USA.

In some embodiments, the detection and avoidance system 100 furtherincludes a second camera channel 114, at which the imaging unit 102captures a second image of a field of view that is substantially thesame as the field of view at which the first image is obtained at thefirst camera channel 112. Given the information of the wavelength atwhich the first camera channel 112 filters radiation, the second camerachannel 114 is configured not to filter radiation at the samewavelength. Accordingly, the objects not emitting or not re-emittingradiation, or areas not illuminated by radiation at the wavelength arenevertheless captured in the second image, not as dark or ON pixelsonly. For example, the second camera channel can be a RGB camera channelat which the second image is captured as a color image. As to theabove-described airborne aircraft example, the aircraft can berepresented in colors in the second image. In some embodiments, thesecond camera channel filters radiation at a wavelength other than thewavelength designated for the first camera channel for filtering.

Upon obtaining the first image, the imaging unit 102 communicates thefirst image to a processing unit 104. In some embodiments, the firstimage is a UV image where a UV filter is utilized at the first camerachannel 112 to filter wavelength in the UV range. In some embodiments,the processing unit 104 includes sub-units for horizon detection 122,object detection 124, object selection 126, and processed object imagegeneration 128 in order to process the obtained first image. Theseafore-mentioned sub-units will be further described in details withreference to FIGS. 2 and 4.

In some embodiments, upon the object selection sub-unit 126 determinesthat one or more detected objects are collision hazardous, theprocessing unit 104 communicates with a notifying unit 108, which inturn communicates the collision hazard information determined based onthe detected one or more objects to the pilot control system 150 of thevehicle. In some embodiments, such selection unit is further enabledwith machine learning capabilities such that the criteria by whichcollision hazardous objects are selected can be trained and refinedbased on data and feedback later provided or gathered by the detectionand avoidance system or by other systems of the vehicle.

In some other embodiments, the processing unit 104 communicates theprocessed first image and the second image to an analyzing unit 106,According to various embodiments, the analyzing unit 106 employs avariety of recognition systems and tools for the purposes of classifyingor identifying the detected objects. The classification oridentification results produced by the analyzing unit 106, as well asthe collision hazard information determined therefrom, are communicatedto the notifying unit 108. In some embodiments, the classificationinformation is fed to train the above-described selection sub-unit formaking selection of collision hazardous objects by use of the UV imageonly. In some embodiments, such recognition or identification systemsand tools are enabled with machine learning capabilities. In someembodiments, the analyzing unit 106 includes a single channel classifier142. In some other embodiments, the analyzing unit 106 includes amultiple channel classifier 144. The single channel classifier 142 andthe multiple channel classifier 144 will be further described in detailswith reference to FIG. 3.

Upon receiving collision hazard information from the processing unit 104or the analyzing unit 106, the notifying unit 108 communicates thecollision hazard information to the pilot control system 150. In someembodiments, the pilot control system 150 is an autopilot control systemfor the vehicle. Given the collision hazard information received,options of avoidance maneuvers, for example, by calculating analternative flight path, adjusting its own velocity or altitude, etc.can be generated accordingly. In some embodiments, avoidance maneuversare determined with consideration of the vehicle's own flight statussuch as the altitude and velocity data. As a result, the pilot controlsystem 150 maneuvers the vehicle to avoid the detected collision hazard.

FIG. 2 illustrates a sequence of intermediate images at various stagesof processing by an example detection and avoidance system for a vehiclein accordance with one or more embodiments of the present disclosure.Starting in stage (A) in the sequence, an incoming UV image 200 capturedat the first camera channel is received by a processing unit of thedetection and avoidance system. The incoming UV image 200 is shown toinclude, in its field of view, example objects such as a cloud object212, an airborne object 220, a horizon 214 having a horizon edge 214A aswell as a number of ground objects 214B (e.g., a house and a car, etc.)connected thereto. For the purpose of simplicity in illustration, onlyone cloud object 212 and one airborne object 220 are depicted herein. Invarious embodiments, either the number of objects of one type of classor the number of object types or object classes is not limited.

At stage (B), a process of horizon detection is performed on the image200 for the purposes of focusing on objects of interest above thehorizon. First, the horizon edge or horizon line 214A is detected so asto determine the pixel regions in the image occupied by the horizon.Next, the region below the horizon edge 214A (horizon region) is filledby use of, for example, flood fill such that the entire horizon regionis in uniformly dark or “ON” pixels (as indicated by the hashed region).In some embodiments, flood fill is performed, for example, from alocation inside outwardly to the horizon edge 214A. In some otherembodiments, flood fill is performed from the horizon edge 214A inwardlyto the inside of the region. Then, the horizon region 214 is grown toadd to the region one or more neighboring pixels until ground objects214B are entirely included or sub-merged in the horizon region. As shownherein, the horizon region 214 is grown upward into the sky area to forman new edge 216 (as indicated by the dotted line), the grown edge 216enclosing all the ground objects 214B (e.g., the house and the car,etc.) below in the horizon region 214 without any ground objectsextruding upward therefrom.

In various embodiments, the horizon region growing can be performed byany region growing processes. For example, the one or more neighboringpixels to be added to the horizon region satisfy a pre-determinedthreshold criterion for being added thereto. As a result of theprocessing at stage (B), an intermediate image 202 containing thehorizon region 214 modified to have the elevated new edge 216 isproduced.

At stage (C), the intermediate image 202 is further processed to removethe entire horizon region 214 below the new horizon edge or horizon line216. As a result, an intermediate image 204 only containing the airborneobject 220 and the cloud object 212 is produced.

At stage (D), the intermediate image 204 is still further processed forthe purposes of detecting one or more objects of interest containedtherein. According to various embodiments of the present disclosure, anysuitable computer vision techniques can be utilized to detect objects inthe intermediate image 204. For example, in some embodiments, ConnectedComponent Labeling (CCL) is used to identify and label objects in theimage. Under CCL, neighboring pixels having density values differing byless than a predetermined threshold are considered connected and beingpart of the same object. Therefore, those pixels are assigned the sameobject label. As shown herein, both the airborne object 220 and thecloud object 212 are identified as connected components by use of CCL.In other words, post the application of CCL, both are the candidatedetected objects of interest in the intermediate image 206. Again, forthe purposes of simplicity, only one cloud and one airborne object areshown to be detected by the CCL technique herein. The number of thecomponents or objects that can be detected by use of CCL is not limited.In some embodiments, the image is preprocessed before the application ofCCL to prevent CCL from growing multiple objects together.

In some embodiments, further morphological operations such as dilationand/or erosion are performed for one or more objects labeled by CCL tohandle or remove the noise pixels from the one or more labeled objects.In some embodiments, such handling by use of the dilation and erosion isrepeated as many times as needed to remove the noise pixels. In someembodiments, the further processed one or more objects are furtherscreened to select for candidate collision hazardous objects. In otherwords, objects that are not likely to be collision hazards when judgedby a variety of rules or criteria are excluded from the one or moreobjects detected. In some embodiments, the selection criterion is thesize of an object. For example, when an object (e.g., the cloud object212) occupies an area of more than a pre-determined threshold, e.g., 30%of the intermediate image 206, such object is considered highly likelyto be a cloud and not likely a mid-air collision hazard. In sonicembodiments, a maximum size for a potential mid-air collision hazard orobject can be computed given the information of the camera resolution,the distance from the object and the velocity of the vehicle.Accordingly, objects of sizes larger than the computed size can beexcluded as too large to be a hazard. In some embodiments, the selectioncriteria, such as the percentage threshold, are derived from empiricaldata. In some embodiments, the selection criteria such as the percentagethreshold is determined by a machine learning system trained withfeedback data.

In some embodiments, the object regions are sorted in accordance withtheir respective sizes. For example, a region can identified as theminimum region, while another region the maximum region identified. Insome embodiments, a pre-determined minimum and/or maximum sizes of theregions can be obtained so that regions of a size smaller than theminimum size, or a size larger than the maximum size are to beconsidered as non-candidate objects. Again, the respective minimum andmaximum sizes can be derived from empirical data, or determined by useof a machine learning system trained with feedback data.

As shown here, the cloud object 212 occupies too large an area in theintermediate image 206 and therefore is not selected as the detectedcollision hazardous object. On the other hand, the airborne object 220is selected as the object detected by the detection and avoidancesystem. Accordingly, the cloud object 212 is removed from the objectslabeled by the process of CCL. As a result of the processing of stage(D), an intermediate image 208 containing only the airborne aircraftobject 220 is produced. In some embodiments, the intermediate image 208serves as a mask or threshold image, which contains and defines onlyregions of ON pixels corresponding to regions of interest in theoriginal incoming first image 200. In other words, the mask image 208can serve as a threshold to assist a region-finding algorithm for thevision based collision detection and avoidance. As shown herein, theairborne aircraft 220 is the only object depicted in the mask image 208.

At stage (E), with reference to the incoming color image 250 captured atthe second camera channel, either a color cut-out image 230 of theairborne object 220, and/or an image 232 combining the intermediateimage 208 and the second image 250 is produced as the final intermediateimage output at stage (F). In some embodiments, based on the mask imagegenerated, one or more first regions are identified in the first image.As the first and second image capture substantially same views, one ormore second regions in the second image corresponding to the one or morefirst regions are identified accordingly. As shown herein, by use of abox 222 for the aircraft object 220 in the first image, a correspondingregion 252 for the same aircraft object 220 is identified in the secondimage 250. In some embodiments, the box 222 is utilized to chip out thebox 252 so as to create the cut-out image 230. In some otherembodiments, a combined image 232 is generated by merging the mask image208 with the second image 250. As shown herein, the combined image 232depicts only the detected object, the airborne aircraft 220, within abox 234.

FIG. 3 illustrates a detailed schematic block diagram of an exampledetection and avoidance system analyzing the detected objects inaccordance with one or more embodiments of the present disclosure. Insome embodiments, a processing unit 302 of a detection and avoidancesystem (not shown) outputs a cut-out image 312 processed from the firstimage and the second image, as described above, to an analyzing unit304. In some other embodiments, the processing unit 302 outputs acombined image 314 processed from the first image and the second image,as described above, to the analyzing unit 304. In some embodiments, thefirst image is a UV image, which represents one or more objects notemitting or re-emitting radiation at the wavelength in the UV range inthe field of view in dark or ON pixels.

According to various embodiments of the present disclosure, when thecut-out image 312 is communicated to the analyzing unit 304 as a resultof the processing of the first and second images by the processing unit302, a single channel classifier 316 is utilized to analyze the cut-outimage 312. In various embodiments of the present disclosure, the singlechannel classifier 316 utilizes a machine learning system that has beentrained to identify and label pixels according to correspondingcategories and classes to categorize the object of the cut-out image. Insome embodiments, the single channel classifier 316 includes, forexample and not limited to, AlexNet, GoogLeNet, or any suitable neuralnetworks. In some embodiments, the neural network system can be aconvolutional neural network. In sonic embodiments, the neural networkmay comprise multiple computational layers.

Such neural network can be trained to categorize a variety of objectclasses, for example but not limited to, various types of aircrafts,birds, etc. The neural network can also produce the results of theprobabilities of a pixel being of an object class. For example, theneural network may generate classification data showing that theaircraft contained in the cut-out image 312 has a probability of 0% ofbeing of Boeing 787 Dreamliner, a probability of 5% of being a F-16fighter jet, a probability of 95% of being a Boeing T-X trainer, and aprobability of 15% of being a GA jet.

According to various embodiments of the present disclosure, when thecombined image 314 from both the processed first image and the secondimage are communicated to the analyzing unit 304, a multiple channelclassifier 318 of the analyzing unit 304 is utilized to analyze thecombined image 314. In various embodiments of the present disclosure,the multiple channel classifier 318 utilizes a machine learning systemthat has been trained to identify and label pixels according tocorresponding categories and classes to categorize the object of thecombined image 314, as well as perform segmentations (e.g., a boundingbox) for the objects. In some embodiments, the multiple channelclassifier 318 includes, for example, but not limited to, DetectNet,FCN-8 (Berkeley), PVAnet, YOLO, DARTnet, or any suitable commercialand/or proprietary neural networks. In some embodiments, the neuralnetwork system can be a convolutional neural network. In sonicembodiments, the neural network may comprise multiple computationallayers.

Given the result vector of the categories and their respectiveprobabilities, the analyzing unit 304 classifies the detected aircraftobject as most likely a T-X trainer and communicates the objectclassification 320 to the notifying unit 306. When a multiple channelclassifier 318 is utilized, object segmentations 324 (the bounding boxfor the aircraft object 220 in the second image) are also communicatedto the notifying unit 306, in addition to an object classification 322.Accordingly, the collision hazard information is determined based on theclassification information or both the classification information andthe segmentation information. For example, a database for thecharacteristic of an aircraft of a T-X trainer can consulted todetermine the maximum speed it is capable of so as to calculate how longit takes the detected T-X trainer to cross path with the vehicle withoutavoidance maneuvers.

FIGS. 4A and 4B illustrate a flow chart of an example method 400 fordetection and avoidance of collision hazards for a vehicle in accordancewith one or more embodiments of the present disclosure. In variousembodiments, method 400 operates a detection and avoidance system 100 todetect and to communicate the detected collision hazards to a pilotcontrol system such as an autopilot control system of the vehicle suchthat the vehicle is maneuvered in avoidance of the communicatedcollision hazard.

At step 402, a first image or image frame of a field of view is obtainedat a first camera channel. With the first camera channel filteringradiation at a certain wavelength, the first image renders arepresentation of one or more objects that do not emit or re-emitradiation at the certain wavelength in dark or ON pixels. The objects inthe field of view that do emit or re-emit radiation at the wavelength,as well as the background illuminated with radiation at the wavelength,are captured as white or OFF pixels. In sonic embodiments, thewavelength is within the ultraviolet (UV) range. For example, objectssuch as, but not limited to, a cloud, an aircraft, a bird, or groundobjects (e.g., houses, high-rising towers, cars etc.) and a horizon arethe objects that do not emit or re-emit radiation in the LTV range. Onthe other hand, when the sky is generally illuminated with radiationfrom the sun in UV range, sky areas in the field of view not obscured byobjects not emitting or re-emitting radiation in the UV range are areasof radiation at the wavelength. The filtering of radiation in the UVwavelength range can be performed by any suitable technologies. In sonicembodiments, the first camera channel filters radiation by use of afilter having a bandpass wavelength range in the ultraviolet (UV) range.

In some embodiments, in addition to the first image, a second image orimage frame is captured at a second camera channel at step 412. Thesecond image or image frame captures a field of view that issubstantially the same as the field of view at which the first camerachannel captures the first image. Contrary to the first camera channel,the second camera channel does not filter the radiation at thewavelength configured for the first camera channel.

At step 404, the first image is processed to detect one or more objects.Those one or more objects may pose potential collision hazards to thevehicle. According to various embodiments of the present disclosure, thefirst image goes through a series of processing stages such as horizondetection 420, connected component labeling 422 to detect objects, andselection of objects 424 from the detected objects. In some embodiments,at step 420, a horizon detection is performed to detect a horizon regioncaptured in the first image. In response to a detected horizon region,the first image is further processed to grow the horizon region in orderto delete the regions together with the ground objects. In someembodiments, the horizon region is flood filled. Next, in someembodiments, at step 426, the horizon region is grown by addingneighboring pixels to include ground objects extending from an edge ofthe horizon region. Lastly, the grown horizon region is removed from thefirst image as a result of the horizon detection so that the first imageis processed into a first intermediate image of the first image.

At step 422, in some embodiments, the one or more objects are detectedby use of connected component labeling (CCL) to process the firstintermediate image. Afterwards at step 424, in some embodiments, fromthe detected one or more objects, selection by a criterion is performedto exclude objects not likely collision hazards. Once the one or moreobjects are further selected, those one or more selected objects areconsidered the detected collision hazardous objects, based on whichcollision hazard information is determined. Accordingly, along a path tostep 410, such collision hazard information determined based upon thedetected one or more selected objects are communicated to a pilotcontrol system of the vehicle such that avoidance maneuvers can beperformed by the pilot control system accordingly.

In some embodiments, the selection of objects employs a machine learningsystem that has been trained to select object by use of UV image only.In some embodiments, the machine learning system may utilize a neuralnetwork system, which may be a convolutional neural network. In someembodiments, the neural network may comprise multiple computationallayers.

At step 428, one or more first regions corresponding to the one or moreobjects selected at step 424 are identified in the first image. Suchfirst region may encompass or enclose the entire object selected. Forexample, a first region can be a bounding box of an object such that thefirst region is a cut-out region or mask region of the enclosed objectin the first image.

At step 430, one or more second regions corresponding to the one or morefirst regions are identified in the second image. As the second imagerepresents a field of view that is substantially the same as which thefirst image represents, regions of pixels in the two images correspondto each other in the sense that they represent the same objects in thefield of view. For example, by mapping the identified one or more firstregions in the first image to the second image, the one or more secondregions are identified.

At step 406, the detected one or more objects are communicated to ananalyzing unit to determine collision hazard information based on thedetected objects. In some embodiments, at step 432, the one or morefirst and second regions identified at steps 428 and 430, respectively,are communicated to an analyzing unit to determine collision hazardinformation. In some embodiments, the above-described cut-out images arecommunicated to the analyzing unit. In some other embodiments, theabove-described combined images are communicated to the analyzing unit.

According to various embodiments of the present disclosure, method 400can employ a machine learning system (e.g., the single channelclassifier 316 and/or the multiple channel classifier 318) to classifythe one or more objects. In some embodiments, the machine learningsystem classifies the type or the class of the one or more detectedobjects. In some embodiments, region segmentations are further producedfor the one or more objects upon recognition.

At step 408, collision hazard information determined based upon thedetected one or more detected objects are communicated to a pilotcontrol system of the vehicle such that avoidance maneuvers can beperformed by the pilot control system. The collision hazard informationmay also include characteristics of the object such as size, speed,heading, and/or other pertinent information derived from theclassification information for the detected one or more objects. And atstep 410, one or more avoidance maneuvers (e.g. evasive maneuvers) areperformed by the pilot control system of the vehicle to avoid thedetected one or more objects accordingly.

FIG. 5 illustrates a perspective view of an example unmanned aerialvehicle (UAV) 500 equipped with an example detection and avoidancesystem and in the vicinity of another aircraft 508, in accordance withone or more embodiments of the present disclosure. The UAV 500 includesa pilot control system 504 communicatively coupled to a detection andavoidance system 502. In some embodiments, the detection and avoidancesystem 502 obtains image frames at the camera channels (not shown)coupled to a plurality of cameras 503 that can be positioned at variouslocations of the UAV 500. For example, the cameras 503 can be positionedat extremities of the UAV 500 including, for example, but not limitedto, the nose and tail end (not shown). For another example, the cameras503 can be positioned and distributed to be forward looking, side-waylooking, upward looking, downward looking, or rearward looking. As shownherein, at the cameras 503, the detection and avoidance system 502captures images of a field of view 506, in which the aircraft 508appears to be approaching from a distance. Based on the image framescaptured for the field of view 506, the detection and avoidance system502 processes and analyzes the image frames, e.g., the first image andthe second image, as above-described, to determine whether and how theaircraft 508 is a collision hazard.

Upon the determination of the collision hazard information, thedetection and avoidance system 502 notifies the determined collisionhazard information to the pilot control system 504 of the UAV 500 suchthat the UAV 500 executes a maneuver to avoid the detected aircraft 508.In some embodiments, the pilot control system is an autopilot controlsystem. In sonic embodiments, the detection and avoidance system 502determines how the aircraft 508 poses a collision hazard so that the UAVis instructed to change its velocity and/or course of flight accordinglyto avoid the aircraft 508. In some embodiments, one or more maneuveroptions are generated based on the collision hazard information and onethat addresses the hazard posed by the object being classified asmultiple classes is performed to best avoid the collision.

FIG. 6 is a block diagram illustrating an example system 600 capable ofimplementing various processes and systems described in the presentdisclosure. In sonic embodiments, system 600 may be a detection andavoidance system, and one or more embodiments may be implemented in theform of a non-transitory computer readable medium storing one or moreprograms to operate the detection and avoidance system. According toparticular embodiments, system 600, suitable for implementing particularembodiments of the present disclosure, includes a processor 601, amemory 603, an interface 611, a bus 615 (e.g., a PCI bus or otherinterconnection fabric), and camera channels 617, and operates to detectand avoid collision hazards for a vehicle, such as within a detectionand avoidance (DAA) system.

Operatively coupled to the processor 601, the camera channels 617 areconfigured so that the system 600 captures images thereat. In someembodiments, when acting under the control of appropriate software orfirmware, the processor 601 is responsible for obtaining a first imageof a field of view at a first camera channel (such as at step 402),processing the first image to detect the one or more objects (such as instep 404), communicating collision hazard information determined basedupon the detected one or more objects to a pilot control system of thevehicle (such as in step 408), and performing maneuvers to avoid thedetected one or more objects (such as at step 410). In some embodiments,the processor 601 is further responsible for obtaining a second image ofa substantially same field of view at a second camera channel (such asat step 412), identifying, in the first image, one or more first regionscorresponding to the one or more objects (such as at step 428),identifying, in the second image, one or more second regionscorresponding to the one or more first regions (such as at step 430),and communicating, the one or more first and second regions to ananalyzing unit to determine collision hazard information (such as atstep 406).

In other embodiments, the processor 601 may be responsible for horizondetection (such as at step 420), and/or detecting one or more objects byuse of Connected Component Labeling (CCL) (such as at step 422), and/orselecting by a criterion, from the detected one or more objects toexclude objects (such as at step 424), and/or analyzing the detectedobjects to classify the objects; and/or or analyzing the detectedobjects to classify the objects and to produce segmentations of theobjects in addition. In some other embodiments, the processor 601 may beresponsible for analyzing the detected objects by use of machinelearning mechanism. Various specially configured devices can also beused in place of a processor 601 or in addition to processor 601.

The interface 611 may be configured to send and receive data packets ordata segments, for example, over a network. Particular examples ofinterfaces supports include Ethernet interfaces, frame relay interfaces,cable interfaces, DSL interfaces, token ring interfaces, and the like.In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases; they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 600 uses memory603 to store data and program instructions for obtaining a first imageof a field of view at a first camera channel (such as at step 402);processing the first image to detect the one or more objects (such as instep 404), communicating collision hazard information determined basedupon the detected one or more objects to a pilot control system of thevehicle (such as in step 408), and performing maneuvers to avoid thedetected one or more objects (such as at step 410). In sonicembodiments, the processor 601 is further responsible for obtaining asecond image of a substantially same field of view at a second camerachannel (such as at step 412), identifying, in the first image, one ormore first regions corresponding to the one or more objects (such as atstep 428), identifying, in the second image, one or more second regionscorresponding to the one or more first regions (such as at step 430),and communicating, the one or more first and second regions to ananalyzing unit to determine collision hazard information (such as atstep 406).

In sonic embodiments, the memory 603 may store data and programinstructions for horizon detection (such as at step 420), and/ordetecting one or more objects by use of Connected Component Labeling(CCL) (such as at step 422), and/or selecting by a criterion, from thedetected one or more objects to exclude objects (such as at step 424),and/or analyzing the detected objects to classify the objects; and/or oranalyzing the detected objects to classify the objects and to producesegmentations of the objects in addition. In some other embodiments, thestored data and program instructions are for analyzing the detectedobjects by use of machine learning mechanism.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the present disclosure. It is therefore intended that thepresent disclosure be interpreted to include all variations andequivalents that fall within the true spirit and scope of the presentdisclosure. Although many of the components and processes are describedabove in the singular for convenience, it will be appreciated by one ofskill in the art that multiple components and repeated processes canalso be used to practice the techniques of the present disclosure.

What is claimed is:
 1. A detection and avoidance system (100) by avehicle (500), comprising: an imaging unit (102) configured to obtain afirst image (200) of a field of view (506) at a first camera channel(112), the first camera channel filtering radiation at a wavelength,wherein one or more objects (212, 220) in the field of view do not emitradiation at the wavelength; a processing unit (104) configured toreceive the first image from the imaging unit and to detect one or moreobjects therein and a notifying unit (108) configured to communicatecollision hazard information determined based upon the detected one ormore objects (220) to a pilot control system (150) of the vehicle. 2.The detection and avoidance system of claim 1, wherein the wavelength iswithin the ultraviolet range, and the first camera channel filtersradiation by use of a filter (112A) having a bandpass wavelength rangein the ultraviolet range.
 3. The detection and avoidance system of claim1, wherein the processing of the first image comprises horizon detection(122).
 4. The detection and avoidance system of claim 3, wherein thehorizon detection comprises growing a horizon region (214) by addingneighboring pixels to include ground objects (214B) extending from anedge (214A) of the horizon region.
 5. The detection and avoidance systemof claim 1, wherein the one or more objects are detected (124) by use ofconnected component labeling (CCL) (422).
 6. The detection and avoidancesystem of claim 1, wherein the processing of the first image furthercomprises selecting (126), by a criterion, from the detected one or moreobjects to exclude objects not likely collision hazards.
 7. Thedetection and avoidance system of claim 1, further comprising ananalyzing unit (106) configured for determining collision hazardinformation based on the detected one or more objects.
 8. The detectionand avoidance system of claim 7, wherein the analyzing unit comprises alearning mechanism (142, 144) to classify the one or more objects uponrecognition.
 9. The detection and avoidance system of claim 1, whereinthe imaging unit is further configured to obtain a second image (250) ofa substantially same field of view (506) at a second camera channel(114), the second camera channel not filtering radiation at thewavelength, wherein the processing unit is further configured toidentify, in the first image, one or more first regions (222)corresponding to the one or more objects, and to identify, in the secondimage, one or more second regions (252) corresponding to the one or morefirst regions; and wherein the detection and avoidance system furthercomprises an analyzing unit (106) configured to determine collisionhazard information based on the one or more first and second regions.10. The detection and avoidance system of claim 9, wherein the secondimage is a color image.
 11. The detection and avoidance system of claim9, wherein the analyzing unit comprises a learning mechanism (142, 144)to classify objects upon recognition.
 12. The detection and avoidancesystem of claim 9, wherein the analyzing unit produces regionsegmentations (234) for the one or more objects upon recognition. 13.The detection and avoidance system of claim 1, wherein the notifyingunit notifies the pilot control system to perform maneuvers to avoid thedetected one or more objects.
 14. The detection and avoidance system ofclaim 1, wherein the vehicle is an unmanned vehicle.
 15. The detectionand avoidance system of claim 1, wherein the vehicle is an unmannedaerial vehicle.
 16. A method (400) of detection and avoidance by avehicle (500), comprising: obtaining (402) a first image (200) of afield of view (506) at a first camera channel (112), the first camerachannel filtering radiation at a wavelength, wherein one or more objects(212, 220) in the field of view do not emit radiation at the wavelength;processing (404) the first image to detect the one or more objects; andcommunicating (408) collision hazard information determined based uponthe detected one or more objects (220) to a pilot control system (150)of the vehicle.
 17. The method of claim 16, wherein the wavelength iswithin the ultraviolet range, and the first camera channel filtersradiation by use of a filter (112A) having a bandpass wavelength rangein the ultraviolet range.
 18. The method of claim 16, wherein theprocessing of the first image comprises horizon detection (420).
 19. Themethod of claim 18, wherein the horizon detection comprises growing(426) a horizon region (214) by adding neighboring pixels to includeground objects (214B) extending from an edge (214A) of the horizonregion.
 20. The method of claim 16, wherein the one or more objects aredetected by use of connected component labeling (CCL) (422).
 21. Themethod of claim 16, wherein the processing of the first image furthercomprises selecting (424), by a criterion, from the detected one or moreobjects to exclude objects that are not likely collision hazards. 22.The method of claim 16, further comprising communicating (406) thedetected one or more objects to an analyzing unit (106) to determinecollision hazard information.
 23. The method of claim 22, wherein theanalyzing unit comprises a learning mechanism to classify the one ormore objects upon recognition.
 24. The method of claim 16, furthercomprising: obtaining (412) a second image (250) of a substantially samefield of view (506) at a second camera channel (114), the second camerachannel not filtering radiation at the wavelength; identifying (428), inthe first image, one or more first regions (222) corresponding to theone or more objects; identifying (430), in the second image, one or moresecond regions corresponding to the one or more first regions; andcommunicating (432), the one or more first and second regions to ananalyzing unit (106) to determine collision hazard information.
 25. Themethod of claim 24, wherein at least one of: the second image is a colorimage; the analyzing unit comprises a learning mechanism (142, 144) toclassify objects upon recognition; and the analyzing unit producesregion segmentations (234) for the one or more objects upon recognition.26. The method of claim 16, further comprising performing (410) maneuverto avoid the detected one or more objects.
 27. The method of claim 16,wherein at least one of the following: the vehicle is an unmanned landvehicle; and the vehicle is an unmanned aviation vehicle.
 28. Anaviation vehicle (500) comprising: a pilot control system (504); and adetection and avoidance system (100) comprising: an imaging unit (102)configured to obtain a first image (200) of a field of view (506) at afirst camera channel (112), the first camera channel filtering radiationat a wavelength, wherein one or more objects (212, 220) in the field ofview do not emit radiation at the wavelength; a processing unit (104)configured to receive the first image from the imaging unit and todetect one or more objects therein; and a notifying unit (108)configured to communicate collision hazard information determined basedupon the detected one or more objects (220) to the pilot control system.29. The aviation vehicle of claim 28, wherein the wavelength is withinthe ultraviolet range, and the first camera channel filters radiation byuse of a filter (112A) having a bandpass wavelength range in theultraviolet range.
 30. The aviation vehicle of claim 28, wherein theprocessing of the first image comprises horizon detection (122).
 31. Theaviation vehicle of claim 28, wherein the processing of the first imagefurther comprises selecting (126), by a criterion, from the detected oneor more objects to exclude objects not likely collision hazards.
 32. Theaviation vehicle of claim 28, wherein the detection and avoidance systemfurther comprises an analyzing unit (106) configured for determiningcollision hazard information based on the detected one or more objects.33. The aviation vehicle of claim 28, wherein the imaging unit isfurther configured to obtain a second image (250) of a substantiallysame field of view (506) at a second camera channel (114), the secondcamera channel not filtering radiation at the wavelength, wherein theprocessing unit is further configured to identify, in the first image,one or more first regions (222) corresponding to the one or moreobjects, and to identify, in the second image, one or more secondregions (252) corresponding to the one or more first regions; andwherein the detection and avoidance system further comprises ananalyzing unit (106) configured to determine collision hazardinformation based on the one or more first and second regions.
 34. Theaviation vehicle of claim 33, wherein at least one of: the second imageis a color image. the analyzing unit comprises a learning mechanism(142, 144) to classify objects upon recognition; the analyzing unitproduces region segmentations (234) for the one or more objects uponrecognition; the pilot control system maneuvers the vehicle to avoid thedetected one or more objects; and the aviation vehicle is unmanned. 35.A non-transitory computer-readable storage medium having one or moreprograms configured for execution by a computer, the one or moreprograms comprising instructions for: obtaining (402) a first image(200) of a field of view (506) at a first camera channel (112), thefirst camera channel filtering radiation at a wavelength, wherein one ormore objects (212, 220) in the field of view do not emit radiation atthe wavelength; processing (404) the first image to detect the one ormore objects; and communicating (408) collision hazard informationdetermined based upon the identified one or more objects (220) to apilot control system (150) of a vehicle.
 36. The non-transitorycomputer-readable storage medium of claim 35, wherein the wavelength iswithin the ultraviolet range, and the first camera channel filtersradiation by use of a filter 112A) having a bandpass wavelength range inthe ultraviolet range.
 37. The non-transitory computer-readable storagemedium of claim 35, wherein the instructions further comprisescommunicating (406) the detected one or more objects to an analyzingunit (106) to determine collision hazard information.
 38. Thenon-transitory computer-readable storage medium of claim 37, wherein theanalyzing unit comprises a learning mechanism to classify the one ormore objects upon recognition.
 39. The non-transitory computer-readablestorage medium of claim 35, wherein the instructions further comprises:obtaining (412) a second image (250) of a substantially same field ofview (506) at a second camera channel (114); the second camera channelnot filtering radiation at the wavelength; identifying, (428), in thefirst image, one or more first regions (222) corresponding to the one ormore objects; identifying (430), in the second image, one or more secondregions (252) corresponding to the one or more first regions; andcommunicating (432), the one or more first and second regions to ananalyzing unit (106) to determine collision hazard information.
 40. Thenon-transitory computer-readable storage medium of claim 39, wherein atleast one of: the second image is a color image; the analyzing unitcomprises a learning mechanism (142, 144) to classify objects uponrecognition; the analyzing unit produces region segmentations (234) forthe one or more objects upon recognition; and the pilot control systemmaneuvers the vehicle to avoid the detected one or more objects.